ECIS 2020 Research Papers
Abstract
Open knowledge collaboration systems must strive to maintain high quality of output to be perceived as a valuable resource by their users. However, maintaining quality requires often rejecting contributions made in good faith by contributors who may not be familiar with collectively established standards. This can demotivate especially newcomers from making further contributions and ultimately diminish the capacity of the system to maintain its contributor base that is typically highly unstable or ’fluid’ in open knowledge collaboration systems. In this paper, we argue that the way in which the rejection is served is an important but little understood factor that mediates the impact of rejection on contributor retention. We harness a discrete change to the user interface of a popular questions and answers service Stack Overflow to show that it is possible to mitigate the negative impact of rejection by making it more legitimate to the user. We estimate a large number of regression discontinuity scenarios to show that the way in which the rejection is served has a causal impact on contributor retention and that the findings are not sensitive to minor changes to non-parametric estimation. We discuss the results against current knowledge of open collaboration systems and the inherent tension between content quality and contributor retention in them.
Recommended Citation
Aaltonen, Aleksi and Wattal, Sunil, "Rejecting and Retaining New Contributors in Open Knowledge Collaboration: A Natural Experiment in Stack Overflow Q&A Service" (2020). In Proceedings of the 28th European Conference on Information Systems (ECIS), An Online AIS Conference, June 15-17, 2020.
https://aisel.aisnet.org/ecis2020_rp/183
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